def test_can_reiterate_sequence(self):
source = Dataset.from_iterable([
DatasetItem('1', subset='a', annotations=[Label(0),
Label(1)]),
DatasetItem('2', subset='a', annotations=[Label(1)]),
DatasetItem('3', subset='a', annotations=[Label(2)]),
DatasetItem('4', subset='a', annotations=[Label(1),
Label(2)]),
DatasetItem('5', subset='b', annotations=[Label(0)]),
DatasetItem('6', subset='b', annotations=[Label(0),
Label(2)]),
DatasetItem('7', subset='b', annotations=[Label(1),
Label(2)]),
DatasetItem('8', subset='b', annotations=[Label(2)]),
],
categories=['a', 'b', 'c'])
transformed = LabelRandomSampler(source, count=2)
actual1 = Dataset.from_extractors(transformed)
actual1.init_cache()
actual2 = Dataset.from_extractors(transformed)
actual2.init_cache()
compare_datasets_strict(self, actual1, actual2)
def _export(dst_file, instance_data, save_images=False):
dataset = Dataset.from_extractors(GetCVATDataExtractor(
instance_data, include_images=save_images), env=dm_env)
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir, 'yolo', save_images=save_images)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
dm_env.converters.get('mot_seq_gt').convert(extractor,
save_dir=temp_dir, save_images=save_images)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
converter = dm_env.make_converter('yolo', save_images=save_images)
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False):
dataset = Dataset.from_extractors(CvatTaskDataExtractor(
task_data, include_images=save_images),
env=dm_env)
with TemporaryDirectory() as temp_dir:
dataset.export(temp_dir, 'tf_detection_api', save_images=save_images)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
envt = dm_env.transforms
extractor = extractor.transform(envt.get('id_from_image_name'))
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
converter = dm_env.make_converter('mot_seq_gt',
save_images=save_images)
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, dst_file)
def __call__(self, extractor, save_dir):
from datumaro.components.project import Environment, Dataset
env = Environment()
id_from_image = env.transforms.get('id_from_image_name')
extractor = extractor.transform(id_from_image)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
converter = env.make_converter('voc',
label_map='source',
save_images=self._save_images)
converter(extractor, save_dir=save_dir)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
envt = dm_env.transforms
extractor = extractor.transform(KeepTracks) # can only export tracks
extractor = extractor.transform(envt.get('polygons_to_masks'))
extractor = extractor.transform(envt.get('boxes_to_masks'))
extractor = extractor.transform(envt.get('merge_instance_segments'))
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
dm_env.converters.get('mots_png').convert(extractor,
save_dir=temp_dir,
save_images=save_images)
make_zip_archive(temp_dir, dst_file)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
envt = dm_env.transforms
extractor = extractor.transform(envt.get('polygons_to_masks'))
extractor = extractor.transform(envt.get('boxes_to_masks'))
extractor = extractor.transform(envt.get('merge_instance_segments'))
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
with TemporaryDirectory() as temp_dir:
converter = dm_env.make_converter('voc_segmentation',
apply_colormap=True,
label_map=make_colormap(task_data),
save_images=save_images)
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, dst_file)
def dump(file_object, annotations):
from cvat.apps.dataset_manager.bindings import CvatAnnotationsExtractor
from cvat.apps.dataset_manager.util import make_zip_archive
from datumaro.components.project import Environment, Dataset
from tempfile import TemporaryDirectory
env = Environment()
id_from_image = env.transforms.get('id_from_image_name')
extractor = CvatAnnotationsExtractor('', annotations)
extractor = extractor.transform(id_from_image)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
converter = env.make_converter('voc', label_map='source')
with TemporaryDirectory() as temp_dir:
converter(extractor, save_dir=temp_dir)
make_zip_archive(temp_dir, file_object)
def _export(dst_file, task_data, save_images=False):
extractor = CvatTaskDataExtractor(task_data, include_images=save_images)
envt = dm_env.transforms
extractor = extractor.transform(envt.get('polygons_to_masks'))
extractor = extractor.transform(envt.get('boxes_to_masks'))
extractor = extractor.transform(envt.get('merge_instance_segments'))
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
label_map = make_colormap(task_data)
with TemporaryDirectory() as temp_dir:
dm_env.converters.get('camvid').convert(
extractor,
save_dir=temp_dir,
save_images=save_images,
apply_colormap=True,
label_map={label: label_map[label][0]
for label in label_map})
make_zip_archive(temp_dir, dst_file)
def test_create_from_extractors(self):
class SrcExtractor1(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
annotations=[
Bbox(1, 2, 3, 4),
Label(4),
]),
DatasetItem(id=1, subset='val', annotations=[
Label(4),
]),
])
class SrcExtractor2(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1, subset='val', annotations=[
Label(5),
]),
])
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1,
subset='train',
annotations=[
Bbox(1, 2, 3, 4),
Label(4),
]),
DatasetItem(id=1,
subset='val',
annotations=[
Label(4),
Label(5),
]),
])
dataset = Dataset.from_extractors(SrcExtractor1(), SrcExtractor2())
compare_datasets(self, DstExtractor(), dataset)
def __call__(self, extractor, save_dir):
from datumaro.components.project import Environment, Dataset
env = Environment()
polygons_to_masks = env.transforms.get('polygons_to_masks')
boxes_to_masks = env.transforms.get('boxes_to_masks')
merge_instance_segments = env.transforms.get('merge_instance_segments')
id_from_image = env.transforms.get('id_from_image_name')
extractor = extractor.transform(polygons_to_masks)
extractor = extractor.transform(boxes_to_masks)
extractor = extractor.transform(merge_instance_segments)
extractor = extractor.transform(id_from_image)
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
converter = env.make_converter('voc_segmentation',
apply_colormap=True,
label_map='source',
save_images=self._save_images)
converter(extractor, save_dir=save_dir)
def test_can_load(self):
class DstExtractor(Extractor):
def __iter__(self):
img1 = np.ones((77, 102, 3)) * 255
img1[6:32, 7:41] = 0
mask1 = np.zeros((77, 102), dtype=int)
mask1[67:69, 58:63] = 1
mask2 = np.zeros((77, 102), dtype=int)
mask2[13:25, 54:71] = [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
return iter([
DatasetItem(id='img1',
image=img1,
annotations=[
Polygon([43, 34, 45, 34, 45, 37, 43, 37],
label=0,
id=0,
attributes={
'occluded': False,
'username': '******'
}),
Mask(mask1,
label=1,
id=1,
attributes={
'occluded': False,
'username': '******'
}),
Polygon(
[
30, 12, 42, 21, 24, 26, 15, 22, 18,
14, 22, 12, 27, 12
],
label=2,
group=2,
id=2,
attributes={
'a1': True,
'occluded': True,
'username': '******'
}),
Polygon(
[
35, 21, 43, 22, 40, 28, 28, 31, 31,
22, 32, 25
],
label=3,
group=2,
id=3,
attributes={
'kj': True,
'occluded': False,
'username': '******'
}),
Bbox(13,
19,
10,
11,
label=4,
group=2,
id=4,
attributes={
'hg': True,
'occluded': True,
'username': '******'
}),
Mask(mask2,
label=5,
group=1,
id=5,
attributes={
'd': True,
'occluded': False,
'username': '******'
}),
Polygon(
[
64, 21, 74, 24, 72, 32, 62, 34, 60,
27, 62, 22
],
label=6,
group=1,
id=6,
attributes={
'gfd lkj lkj hi': True,
'occluded': False,
'username': '******'
}),
]),
])
def categories(self):
label_cat = LabelCategories()
label_cat.add('window')
label_cat.add('license plate')
label_cat.add('o1')
label_cat.add('q1')
label_cat.add('b1')
label_cat.add('m1')
label_cat.add('hg')
return {
AnnotationType.label: label_cat,
}
parsed = Dataset.from_extractors(LabelMeExtractor(DUMMY_DATASET_DIR))
compare_datasets(self, expected=DstExtractor(), actual=parsed)
